{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Testing single muscle and neuron models\n",
    "\n",
    "February 24th, 2017\n",
    "\n",
    "Stephen Larson\n",
    "\n",
    "Here we show a few examples of testing the latest configurations of the muscle model and a neuron model.  We'll first look at an example running a muscle and a neuron that are not connected, and we'll put input into just the muscle.  Then we'll look at an example where the muscle and the neuron have a synapse in between them, we stimulate the neuron, and the muscle gets stimulated.\n",
    "\n",
    "## Boilerplate\n",
    "First, some boilerplate imports and magic.  We're going to be using the 'runAndPlot' method to run c302 with just a set of parameters.  Be sure to only run the first cell once!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "os.chdir(\"..\") \n",
    "from runAndPlot import run_c302\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Running a current clamp on the muscle model\n",
    "\n",
    "Next we are going to start off by just running the simplest version of the muscle model.  \n",
    "\n",
    "We're calling this using the code `IClampBWM` which is standing in for \"Current Clamp of the Body Wall Muscle\".  The details of how the current clamp is configured is in the file \"c302_IClampBWM.py\".  \n",
    "\n",
    "We are using parameters \"C2\" which corresponds to a variant on the conductance-based model version, which is pulling from \"parameters_C2.py\" in the c302 directory.  \n",
    "\n",
    "Then the model is simulated for 1000 steps at a 0.05 timestep.  We will use \"jNeuroML_NEURON\" which means we'll be simulating this with the jNeuroML interface to the NEURON simulation engine.   \n",
    "\n",
    "Here we go:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************\n",
      "\n",
      "   Going to generate c302_C2_IClampBWM and run for 1000 on jNeuroML_NEURON\n",
      "\n",
      "********************\n",
      "Set default parameters for C\n",
      "Set default parameters for C2\n",
      "Opened file: /home/developer/forks/CElegansNeuroML/CElegans/pythonScripts/c302/../../../herm_full_edgelist.csv\n",
      "Opened file: /home/developer/forks/CElegansNeuroML/CElegans/pythonScripts/c302/../../../herm_full_edgelist.csv\n",
      "c302      >>>  Positioning muscle: MDR01 at (80,-270,80)\n",
      "c302      >>>  Writing generated network to: /home/developer/forks/CElegansNeuroML/CElegans/pythonScripts/c302/examples/c302_C2_IClampBWM.nml\n",
      "Validating examples/c302_C2_IClampBWM.nml against /usr/local/lib/python2.7/dist-packages/neuroml/nml/NeuroML_v2beta4.xsd\n",
      "It's valid!\n",
      "(Re)written network file to: examples/c302_C2_IClampBWM.nml\n",
      "c302      >>>  Finished simulation of LEMS_c302_C2_IClampBWM.xml and have reloaded results\n",
      "c302      >>>  Reloaded data: ['MDR01/0/GenericMuscleCell/caConc', 'AVAL/0/GenericNeuronCell/caConc', 'MDR01/0/GenericMuscleCell/v', 't', 'AVAL/0/GenericNeuronCell/v']\n",
      "c302      >>>  All cells: ['AVAL']\n",
      "c302      >>>  Plotting neuron voltages\n",
      "c302      >>>  Generating plots for: Membrane potentials of 1 neuron(s) (IClampBWM C2)\n",
      "c302      >>>  Plotting muscle voltages\n",
      "c302      >>>  Generating plots for: Membrane potentials of 1 muscle(s) (IClampBWM C2)\n"
     ]
    },
    {
     "data": {
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LImLT7nO2ts8nNoily8o94Peue5fPjohpvVneQDeggsF6wzdmp49+jdc2HcIbo8XyUbBi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\nmZnhYGBmZjgYmJkZDgZmZgb8fzKK0EwMVykLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f471821c410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MrLmnVhqbwI7Mkr4AODNt/nrg+nby7d2Fcs9ML7cvSFVAk/wMyDk3SOXSC24p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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46edc78f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NOMXK3E7cZmZ+OqCZWbG4xW1mViTu4zYzKxo/q8TMrHjcVWJmViDhny4zMyse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\n48RtZlYw/w8h8TnG3ir8WwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46eb670750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5ryi7r/dHWPEpM2rZeKIUS6q23zs3EfXMtfCLtchN73husoaJeiVMw6SMvXPN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gDYDeC7nZkFwBj7MGNsC2NsO7z7/2HG2C+hx+YBAEQ0SETD/DW8++Ip9OD9xRg7C+AUEV3kH/pZ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\nBgZSGEFhYGBgYCCFERQGBgYGBlL8f4Gg6G75iamNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46eb6a1410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cells, cells_to_stimulate, params, muscles = run_c302('IClampBWM','C2','',1000,0.05,'jNeuroML_NEURON',verbose=False,plot_ca=False, data_reader=\"UpdatedSpreadsheetDataReader\", config_package=\"notebooks.configs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Interpreting the results\n",
    "\n",
    "We get four graphs out.  The first two show output from a neuron that has been put into the model, AVAL.  \n",
    "\n",
    "The second two correspond to output from a muscle cell, MDR01.\n",
    "\n",
    "Each pair of graphs are showing two aspects of the membrane potential of the cell they are looking at, both a heat plot and a line graph.  The two plots are showing the same data, just with different visualizations.\n",
    "\n",
    "Given these outputs, I'm not sure that the AVAL neuron is being stimulated at all.  Having a look at the \"c302_IClampBWM.py\" file, I see the following lines:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "```python\n",
    "for i in range(len(stim_amplitudes)):\n",
    "    start = \"%sms\"%(i*1000 + 100)\n",
    "    for c in muscles_to_include:\n",
    "        c302.add_new_input(nml_doc, c, start, \"800ms\", stim_amplitudes[i], params)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "These are the lines where the current clamp is actually set up.  But since the loop only goes over \"muscles_to_include\", I think AVAL is not being included in getting stimulated.\n",
    "\n",
    "That's OK -- because the graph for AVAL also suggests that it isn't receiving input.  That checks out.\n",
    "\n",
    "However, it looks like the muscle cell model is receiving input and it is creating spikes.  Cool. \n",
    "\n",
    "### Biological parameters used\n",
    "\n",
    "Next we can check out what the biological parameters that were used in this run were:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Known BioParameters:\n",
      "    BioParameter: cell_diameter = 5 (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: muscle_length = 20 (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: initial_memb_pot = -60 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: muscle_initial_memb_pot = -28 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: specific_capacitance = 1 uF_per_cm2 (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: muscle_specific_capacitance = 1 uF_per_cm2 (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_spike_thresh = -55 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: muscle_spike_thresh = -10 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: muscle_leak_cond_density = 0.0172 mS_per_cm2 (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_leak_cond_density = 0.002 mS_per_cm2 (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: leak_erev = -60 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: muscle_leak_erev = -13 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: muscle_k_slow_cond_density = 0.564 mS_per_cm2 (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_k_slow_cond_density = 0.45833751019872582 mS_per_cm2 (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: k_slow_erev = -60 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: muscle_k_slow_erev = -70 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_k_fast_cond_density = 0.042711643917483308 mS_per_cm2 (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: k_fast_erev = -70 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: muscle_ca_boyle_cond_density = 0.284 mS_per_cm2 (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_ca_boyle_cond_density = 1.812775772264702 mS_per_cm2 (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: ca_boyle_erev = 10 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: muscle_ca_boyle_erev = 46 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: ca_conc_decay_time = 13.811870945509265 ms (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: ca_conc_rho = 0.000238919 mol_per_m_per_A_per_s (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_to_neuron_exc_syn_conductance = 0.49 nS (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_to_muscle_exc_syn_conductance = 3.46 nS (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: exc_syn_delta = 5 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: exc_syn_vth = 00 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: exc_syn_erev = -10 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: exc_syn_k = 0.5per_ms (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_to_muscle_exc_syn_delta = 5 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_to_muscle_exc_syn_vth = 00 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_to_muscle_exc_syn_erev = 00 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_to_muscle_exc_syn_k = 0.025per_ms (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_to_neuron_inh_syn_conductance = 0.29 nS (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_to_muscle_inh_syn_conductance = 1.29 nS (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: inh_syn_delta = 5 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: inh_syn_vth = 0 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: inh_syn_erev = -70 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: inh_syn_k = 0.015per_ms (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_to_muscle_inh_syn_delta = 5 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_to_muscle_inh_syn_vth = 0 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_to_muscle_inh_syn_erev = -50 mV (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_to_muscle_inh_syn_k = 0.025per_ms (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_to_neuron_elec_syn_gbase = 0.01252 nS (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: neuron_to_muscle_elec_syn_gbase = 0.00152 nS (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: muscle_to_muscle_elec_syn_gbase = 0.0052 nS (SRC: BlindGuess, certainty 0.1)\n",
      "    BioParameter: unphysiological_offset_current = 5.135697186048022 pA (SRC: KnownError, certainty 0)\n",
      "    BioParameter: unphysiological_offset_current_del = 0 ms (SRC: KnownError, certainty 0)\n",
      "    BioParameter: unphysiological_offset_current_dur = 2000 ms (SRC: KnownError, certainty 0)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print params.bioparameter_info(\"  \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "There are actually more parameers in here than meets the eye, because there are also neuron-to-muscle and muscle-to-muscle synaptic parameters which don't play a role in generating our output but that's also OK.  We can see that there are muscle and neuron ion channel parameters that are playing a role here.\n",
    "\n",
    "## Connecting the neuron and the muscle\n",
    "\n",
    "Now let's run an example where we actually do have a synapse between the neuron and the muscle.\n",
    "\n",
    "Much of what we had before stays the same, with the change that now we are running the NMJ configuration of the model, which stands for \"Neuromuscular junction\", a name for the synapse between a neuron and a muscle, which is stored in \"c302_NMJ.py\".  \n",
    "\n",
    "In this case we are applying a similar current clamp input as the first time, but we are doing it into only the neuron and not the muscle.  Also, the neuron is now VB1, a motor neuron, and the muscle is MVL07."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************\n",
      "\n",
      "   Going to generate c302_C2_NMJ and run for 1000 on jNeuroML_NEURON\n",
      "\n",
      "********************\n",
      "Set default parameters for C\n",
      "Set default parameters for C2\n",
      "Opened file: /home/developer/forks/CElegansNeuroML/CElegans/pythonScripts/c302/../../../herm_full_edgelist.csv\n",
      "Opened file: /home/developer/forks/CElegansNeuroML/CElegans/pythonScripts/c302/../../../herm_full_edgelist.csv\n",
      "c302      >>>  Positioning muscle: MVL07 at (-80,-90,-80)\n",
      "VB1-MVL07 2 exc Acetylcholine\n",
      "c302      >>>  Writing generated network to: /home/developer/forks/CElegansNeuroML/CElegans/pythonScripts/c302/examples/c302_C2_NMJ.nml\n",
      "Validating examples/c302_C2_NMJ.nml against /usr/local/lib/python2.7/dist-packages/neuroml/nml/NeuroML_v2beta4.xsd\n",
      "It's valid!\n",
      "(Re)written network file to: examples/c302_C2_NMJ.nml\n",
      "c302      >>>  Finished simulation of LEMS_c302_C2_NMJ.xml and have reloaded results\n",
      "c302      >>>  Reloaded data: ['VB1/0/GenericNeuronCell/v', 'MVL07/0/GenericMuscleCell/v', 'VB1/0/GenericNeuronCell/caConc', 't', 'MVL07/0/GenericMuscleCell/caConc']\n",
      "c302      >>>  All cells: ['VB1']\n",
      "c302      >>>  Plotting neuron voltages\n",
      "c302      >>>  Generating plots for: Membrane potentials of 1 neuron(s) (NMJ C2)\n",
      "c302      >>>  Plotting muscle voltages\n",
      "c302      >>>  Generating plots for: Membrane potentials of 1 muscle(s) (NMJ C2)\n"
     ]
    },
    {
     "data": {
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QeH4WcFbdMjcAB+XHI0jDQzRgbxB2YN2EPAeYkB9PIF2wAnAR8O765YB3AxcV\nytdZrrd6+xn7dcAbh0rMwEbAn4ED8n4dUX9cNNr/9cdKcblCmRrV28c4JwG/AN4AXN+s3jLEm187\nj/UTcmmPC2Bz4BHq/rfLHHMZpnZ3WWwLzC88X5DLelwmIlYDzwJbtjmOZraOiEX58WPA1vVxZbXY\nW3lPzerdIPmr8b6kFmepY85f/+8EFgM3kVqLz+T9W7/+Rvu/lZi3bFJvX3wROAOo3XmmWb1liBfS\nzctulHSHpJNzWZmPix2BJ4Bv566hb0nauOQxD7oX9Um9SB+nbR/31996JW0C/Aj4cET8vZ11N9Kf\neiOiKyL2IbU89wf+oZ2xtZOkNwOLI+KOwY6lj14TEfsBRwKnSvrH4swSHhcjSN2FX4+IfYHnSV0J\n7ai7qYGqtxPanZBbuVRwzTKSRpC+2jzZ5jiaeVzShLz+CaRW3TpxZbXYW738sVG9fSJpJCkZfz8i\nfjwUYq6JiGeAX5G+mo/N+7d+/Y32fysxP9mk3la9GniLpHnAlaRuiy+VOF4AImJh/rsYuIb0wVfm\n42IBsCAibs3PryYl6DLHPOjanZBvB3bNZ5ZHkU6CTK9bZjpQO1P6TuCX+ROtU4rrP4HUT1srPz6f\n7T0QeDZ/BboBOEzSFvnM7WG5rNV6WyZJwCXAfRHxhSES81aSxubHLyH1ed9HSszvbBBzT/u/18tM\n83KN6m1JRJwVEZMiYgfS8fnLiDi2rPECSNpY0qa1x6T9eQ8lPi4i4jFgvqTdc9EhpCvWShtzKbS7\nU5p0tvQBUj/i2bnsP4G35MdjgKuAuaQDeKeB6iAHfgAsAlaRPrFPIvXr/QJ4ELgZGJeXFfDVHPfd\nwJRCPf+c450LnFgo/1ZtuUb19jHe15C+as0C7szTUSWPeW/gLznme4BP5fKd8v6dm/f36N72P+nO\nWQ+RTtAcWSifAUxsVu8GHh8Hs3aURWnjzXXclafZrP2/Ku1xkevZB5iZj41rSaMkSh3zYE++dNrM\nrCRe1Cf1zMzKxAnZzKwknJDNzErCCdnMrCSckM3MSsK/qWcNSaoNJYL0A49dpMthAZZFxKsGYJ37\nAqdFxEn9rOc0UoyXticys4HnYW/WEknnAM9FxAUDvJ6rgHMj4q5+1rMR8IdIl+2aDQnusrANIum5\n/PdgSb+RdJ2khyWdL+lYpXsk3y1p57zcVpJ+JOn2PL26hzo3BfauJWNJ50j6rqTfSXpU0tslfTbX\n+/N8mTlDaQ10AAABmklEQVR5nffm++heABARy4B5kvbv1DYx6y8nZGuHVwCnAC8DjgN2i4j9SVdS\nfTAv8yXgwoh4JfCOPK/eFNLVfkU7k+438RbgcuBXEbEX8ALwptyt8jZgz4jYGzi38NqZwGv7//bM\nOsN9yNYOt0e+9aGkh4Abc/nd5J9HBw4F9ki36wBgM0mbRMRzhXomsLaPuuZnEbFK0t2kH0D4eaHu\nHUj3M14OXKL06x/XF167mBLfec6snhOytcOKwuPuwvNu1h5jw4ADI2J5k3peIN07Yr26I6Jb0qpY\ne9Kjm3Tj99W5W+IQ0k18TiO1qMl1vbAB78dsULjLwjrlRtZ2XyBpnx6WuQ/YpS+V5ntHbx4RM4CP\nkLpPanZj/S4Qs9JyQrZO+RAwJZ94u5fU57yOiLgf2Lx2q8kWbQpcL2kW8HvSb+XVvJr0CyZmQ4KH\nvVmpSPoIsDQiejrp15d69gVOj4jj2hOZ2cBzC9nK5uus2ye9ocYD/96Gesw6xi1kM7OScAvZzKwk\nnJDNzErCCdnMrCSckM3MSsIJ2cysJP4/gLRy5JPbIR8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46ed770b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HgCmY9SgahqreDNzcyHOOIAMpEY4IykZv7csxr6eDy049mN6hQqluf6NJBVVt\nbSjVFXL0AbCNeiqH+brZjqiZ2Y5eDqBydlSdBAzCnf6do+L6jGIK8LiI3MfuPop6hsc6QVHNQ3P4\n3PICOfN62smkU0xuj29om45QvyYsvOcqxu4aJaS0vvKMlZJd39pH4WhDiF7ryd2WRKkemwS1Fx82\niuLTdZfCUcIHpbM1w3EHTOfOJzdx1IKemKUqr5NhQzGYFblKytLZ6PpzHNVH4XLvauuYd/2awBjy\nKOomyfhx0pktIqKGv9Q6pj6iOYCWO4Cvn/sSHl6znSPmV1+9rBEYZ7atonDXkQ32PorwEXHVhBYl\nM9txPWFteirh6DWBKPdXEoiv1lM1+8ntIvJBEdktm01EWkTktSLyE+D8+ooXL0XV0jPQ09nCqw+c\nEa9AAWmJNqNw+DmO4KMIj6+vPGMlama2y9ck1GLFGhcmCbWemqp6rGUoeT2oZno6BXgXcE0QproN\naMcolz8AX1XVB+ovYny4at+PYnrSBMwootV6crMt0XwU6mw7IPpsx92WAFGrxzrcGCed2ao6AHwL\n+JaIZIHpQP+etCSqq/Z921E4mFGhi8ouxNpH4Xj12LDjt1V6rrYDIsyOEjAMt55RNFFb6oHtwkU5\nYG2dZXGOouLkcCmdipJH4fiMgmh5FK62JFTGtgUOXW0HRJsdVR7vIhG9LZ5RcDR9yREc7WRTKaFg\n7cx223Nqu1Sl66W5y0uh2s4oHG0IzZdHEa2Eh7vXxSzCFM+v7hVFFYrqptkmLVLT0ViJi8ouxPZB\nLmeYu9mWsrmm9rGKOtwdjSWPwt3W2K5w14xKbyLxiqIKRUedjumIMwoXlV1IFH8LODw5ipKZ7brt\nKaAZ8ihsgyXK/1A/WcZLnJVwq+VR7GRkuYJkR51UN6kcwdWop9ABXCwqqRoCuuqQD4kc9eRoW6Le\nJ262whDFjAbumgOhGcuMx3PualFP3Y0UxEXU0YD3dNArFVRJ1ehyigmwh9vlHoS1ntwkUh6F4+uv\nN9Oa2WJppk1K3rDLRQEBEJGZQGnRaFVdVReJHCG8cVycUZQURVHJ1qhsrs6bnuxGfK47syOtmY27\n7YDKGYXd8Q43JXLZC5evi8Q4ParpoxCRM0XkSWAl8BfgGeCWOssVOy6v4xAqCrslRN1sQ4jtCneu\nL+kabUbheOcavDZD6QshxgJJE0yctZ5snNmfBV4OPKGq+wAnAvfUVSoHKJe1do+0lGcUtXDdmR15\nTWNH2xJIUn4nAAAgAElEQVQpM9v5hYvMq60z2+GmxNq5TjRmbQ13w2NzqroZSIlISlVvBxbVWa7Y\nKS+U495TEMpks0hO0/goHE+4i5yZXW+BxkGU9b9dJ4i8iXS8q9iaaetybotjtolIF/BX4Oci8jWg\ndzwnFZE3i8gyESmKyKJh+y4TkRUislxETh7PecaDyyvDpQOZbEJk1fmigLa2cNfzKMxrM/gobEUL\nm+piCHmI7YwiCTrR1kxbD2wUxVlAP/BR4PfAU8AZ4zzvI8CbMMqnhIgcilkf+zBMUcJviUhjFqIe\nhstF6Cqd2bUwIb7utSFEIs4oHJzgAdEcwOYYRxtCdGe2y8QZUjrRRJ0dTSQ1o55UtXL28JOJOKmq\nPgYjjg7PAn6pqoPAShFZARwD/H0izhuFsvO00WeuTSqKM9t1HwV2o/Ciw4obomYzuz3Ls22Lum4P\nJMyjiGB6cvjCOOnMFpG7gtedIrKj4m+niOyokzxzgecqPq8Oto0k30UislhEFm/cuHHCBckXzCXJ\npN1LXk9HsCG7XhTQJA/ax7m72hQpjcKbwUdhXptiIG5p2kxKTohzMwpVfVXwOqbEOxG5DZg9wq7L\nVfX6sXxnJap6FXAVwKJFiyb818sHnuJs2r1HOkpdIecXLkqBFmofV7aHu0uUnBCXr4mtMzsBEwrr\nWk9JwJie4jl3TdOTiFytqu+otW04qnrSGORZA8yv+Dwv2NZw8kEvnHbQblMyDVhmnLo+o4jio3C5\nV4qSZe6qCQ3KP3Ez2PajJqm5e1XcLwp4WOUHEckAR9dHHG4AzhWR1mBVvQOAe+t0rqrkCsGMIuWe\n6SmKs9H1hDuwr7gKbrfFrM9c+zjXZxTl39i21pO7jbH1USRBKdoOROpy7tF2BGGqO4EXV/ongPXA\nuExHIvJGEVkNHAvcJCK3AqjqMuBa4FFMhNXFqjaGiYknjCjKuGh6Cq6aTXis86Ynax+FeXW4KdYR\nNorr7TCvcYViTiSR16Nw+cLgYPVYVf0c8DkR+ZyqXjaRJ1XV3wG/G2XflcCVE3m+sZAruGt6ilIu\nwv2Eu6i1nhxvSxMsXFROHqx+XCKUdzP5KFysHhuiqpeJyFxg78rjVfWvo/9X8ik7s102PdmFYjqo\n60pErh7rcFuEaG1xlXLyoNty2hC5RIzDmOviWNRTiIh8HpME9ygQmoGUYclyzUYpPNbBXjZa1FMC\n7PoWpUiSMHpNWfoocNxHEf7INWcUiVDeEavHOnyH2frA6oFNmfE3AgcFSXB7DHmXfRQRRnzOJ9xZ\nzyjKx7tKKoqPwt1mVKyZnYRxdg2aKjPb7aKATwPZegviGvkg6injYNRTKc69KYoC2h3ncu2tEhJh\n4SKXR67Ba1MshRq81upgk9CWOIsC2swo+oClIvInoDSrUNUP1U0qB2iWGYX7CxdZhvwlwvRkGcGF\n2wqvnGVue3wdhRknlSXTbeR0uS3EWBTQRlHcEPztUZR9FO7NKCItXOR4wp117oHj1WMhYmZ23aUZ\nO7YDkQQMwssRXDWOS4KZzfWigD8RkXZggaoub4BMTpAL7DpuzigiOLMdT7iLWj3W3ZZEieByW+FF\nrfXktBmtNKNwPXulNnHeMjZLoZ4BLMUkwCEiR4pI088wCsGMwsXM7CiVSouOD19TliUxy2XG3W2M\nfWa2unxJrAscxjW6jUK0HHO3sQ2WqMu5LY65AlPqexuAqi4F9q2jTE4Q5lG4nHBnaw93sAklUpYO\n4CQ4s6NkZrusKaLWenL7mpjXZnHMO1fCo4Kcqm4fts0i3ibZhJnZLlePLVjlHzjuo8B+VTjXsc3M\nxu1JXnlGUeNXT8I1sW1L+fh6SjM+4iwKaOPMXiYibwXSInIA8CHgb/UVK34KDlePjZZH4ba5JvKa\n2e42JVJmtss+ipTlKDxJNENbRCQ2p7vNjOKDmAqyg8AvgO3Ah+splAuUqse6WMIjYtSTw31ShPo1\ngenJ4bG47frfjruNSr9xrZleEjpflwdJUbGdfdcDmxnF6ap6OXB5uEFE3gz8um5SOYDT1WOjlBl3\nfkZhX0gPypVzXUQs49xdLzO+e6SQzfHuNsbWR1E63mEVLgIak9Hf5rEbqXLshFaTdZFck5ieXE+4\ns40Ucn3NbIhSgM7xzOzS/VXrSPenFOWoJ/dlrYWphOtYHoWInAqcBswVka9X7JoE5OstWNzkHV64\nqLxUZe1jXU+4a6bqsVH8LS63QyIGlTrclAhRT+4rEttlg+tBNdPT88Bi4ExgScX2ncBH6ymUC7ht\nejKvNkuhFouOd0pit15AMhLuLDOz6y7J+AjHRs0RUmqXmV063uEbzDZYoh5UW7joQeBBEfkF5vk8\nMNi1XFVz4zmpiHwBOAMYAp4CLlDVbcG+y4ALMSXNP6Sqt47nXGMl1yQlPFzPArb2UQSvDjcloo/C\n3YbYOrNLx7vbFGt/SyKUXoxFAW16wVcATwLfBL4FPCEirx7nef8IHK6qLwaeIPB5iMihmLUvDgNO\nAb4lIulxnmtMlKrHOjmjsH+Qm8VHET7oTnewlsmD4HZmdik8tgnyKEKSJOtoxLnCnY2i+DLwelU9\nXlVfDZwMfGU8J1XVP6hq6Oe4B5gXvD8L+KWqDqrqSmAFJiu84ZSqxzrYy0Yt4dEUPopEmJ7sy5E4\nfEkir5nttmM+YiXcOsoyXuIsCmijKLKVxQBV9Qkmdn2KdwG3BO/nAs9V7FsdbHsBInKRiCwWkcUb\nN26cQHEM+WKRdEqcHMFGKeHhfnisbdkL92cUtuVIjDmw/vKMHfv7y3XK5UiSPzuK0/Rkk0exWES+\nD/ws+Pw2jJO7KiJyGzB7hF2Xq+r1wTGXYyKofm4nbhlVvQq4CmDRokUT/vvlC+rkbAKiLoXqesKd\nbZhvcHyd5RkP1pnZji9cZHvbJyJbPmoehcONMTOKeM5toyjeD1yMKd0BcCfGV1EVVT2p2n4ReSfw\nBuBELav7NcD8isPmBdsaTr6oTmZlQ/lBLlhoCnV8RmGWd6x9XFI6pWZYCrUcfp2EcXZ1mq56rGt5\nFCGqOigi3wD+hCkGuFxVh8ZzUhE5BbgEOF5V+yp23QD8QkS+DOwFHADcO55zjZV8oehksh1EL+Hh\naDOAaOYac7y7jUlFiXqqvzhjxrbWU8kcWGd5xoN9yfRGSDM+ROyWP64HNRWFiJwOfAcTxirAPiLy\nXlW9pfp/VuUbQCvwx+BC3qOq71PVZSJyLfAoxiR1sWo8KSa5ojpZORailvBwvABdym5GkYTRrX1m\nNk5PKaKGx7pM9EWYXMYu56ge2JievgScoKorAERkP+Amyg7oyKjq/lX2XQlcOdbvnigKBXV3RhGp\nhIfTfZJ9SGkCTE8p6+RBt8Njo+YeuHxNys7sWMWYEKzL2Nfj3BbH7AyVRMDTmOzspiZXLDqZbAdR\n8yjcN9fsaVFP4HjnGnEU7jTWa2u431rX16NYLCI3A9di7p03A/eJyJsAVPV/6ihfbOQL7pqeoudR\n1FmgcSA0T9QTTeKjiG7Xd7c1JcmaIcvcxaKAFbQB64Hjg88bgXZMCQ4FmlJRFIoum56CGYVNracE\nJNxFqY/kdlsiVI91vB3QHOYa29lREtrq9IxCVS9ohCCukSsUnQ2PLdd6qn1s0fG6Qs20Zra1Gc31\nGYXtwkUJqOhbKgqYAEVQC9tBVV3OHdN5nadQVCfrPEE005P7tZ5M52rtOG2ATGMlihnN6c7VstZT\nEojaFpcHVUTwgU00XlGMQq6opB13ZjdDCQ/r9QJK/1BPacZHlLU1XM7Mtl/DITi+vuKMC9uopySo\nRIHYBHWzJ3SAfKFI1tGheDOV8CgpvVoHhqYnh7sl68xsxenetWyuSUL3WZ1miuBy2vQkIrNE5Aci\nckvw+VARubD+osVL3mHTU9Q8CpdnFLZtKTuz6yvPeLAP9XVaT1SUGbfDZXNNsyk9l01PPwZuxZTU\nALN+xEfqJZAr5Avu5lGED6ZNrSfXZxS2dYXCCC+nO6UIyYMON6N8TZoiNdu81LwsCVAkcRYFtOkJ\np6vqtZg6TwTrSMS0cmvjcHlGEUY92Zo5XJ5RRPVRuNuSKKG+jvsoglfbvtXdlkQrd+PwYwIEgR8x\nGZ9sFEWviEyjVKJGXg5sr6tUDmDKjLs5o4hienI94c72QU5EuQjbAoeOzyiidK6uY7taXxJwOo8C\n+Bimqut+InI3MAM4p65SOUC+WGya9ShcnlGUSqZb+iicHonbZmbjtqLAciCShDwK22clCWrEtiR/\nPbBJuLtfRI4HDsLcQstVNVd3yWImX3DX9BSthIfbdn3bUN/SmtluTvKAQOntQQsXJYEoz4rrzbat\nTlwPbGYUYNatXhgcf5RJktKf1k0qB3B74SL7SI4kJNxB7Tr7SbGHN8OMwjbAIBnmwGimTZdJicNL\noYrI1cB+wFLKTmwFmltRuLxwUSnqqfaxRcft4enIZg53GxO1wKGr2CapJYFy3arkN8Z2qd16YDOj\nWAQcqhP4S4vIZ4GzMJFUG4B3qurzYnqBrwGnAX3B9vsn6rxRcHvhIvNqX8LDzXZAebW+mj6KBMwo\novko3G2JbRJkErreKP48l68JxOvMtrGtPALMnuDzfkFVX6yqRwI3Av8WbD8Vs/zpAcBFwLcn+LzW\nuFw9VkSs7ZVJ8VHYJtw53BT7hWUSsnCR9doaDrcmlMx2xuoy4rLpCZgOPCoi9wKD4UZVPXOsJ1XV\nHRUfOym3/yzgp8Hs5R4RmSIic1R17VjPNVZyDifcgZ09POy0HNV3QPTwWKdnR1Eys91tRoRaT0no\nXJsn1Fcs7696YKMorqjHiUXkSuA8TE7GCcHmucBzFYetDrY1XFEUiupseCzYlecuJqJzNa+1ssyT\nsma2dR5FA+QZK5HLXjjcmChmWoebAYSZ2Y76KFT1L2P5YhG5jZFNVper6vWqejlwuYhcBnwA+HTE\n778IY55iwYIFYxGxKiY81t0ZhYjUtOsXkzCjSNmZnkIc1nnRMrMdbkjkir4OE3XG6jJOm56CTOz/\nBg4BWoA00Kuqk6r9n6qeZCnDz4GbMYpiDTC/Yt+8YNtI338VcBXAokWLJvz3yxeLzjqzIbSHVz+m\nvNiPy+2wDY8N2uLwuK9ZZhRRHMDgdlsi5VG43BBC06a7JTy+AbwFeBKzBOq7gW+O56QickDFx7OA\nx4P3NwDnieHlwPY4/BPFolJUnHVmA6RFahZtS4JdP5y0NUvMvvUKdw63w7qibwJG4dZl7BOACb+O\n59xWCXequkJE0qpaAH4kIg8Al43jvJ8XkYMw4bHPAu8Ltt+MCY1dgQmPvWAc5xgz+eBquJpwB3bO\n7KQsHwr2JTxcVnr2a2aDy+PwKFF14fGuYjujSIIikRhnFDaKok9EWoClIvJfGMfyuHpQVf2nUbYr\ncPF4vnsiyAd2EJdnFDZmjvKMogECjRHbLPOS0qu7RGPHOjPb8dLvYNpSS3knoXuNUsXAZbNmSFy/\nuE2H/47guA8AvRgfwogdfbMQziicjnpK1R5dlJ3ZDrfDtmhbEkxPRMk9cJu0pdIDt9tSnlFUPy4x\nZjQXTU8ikgb+Q1XfBgwAn2mIVDGTLyRAUVhFPZlXl00DtuGx5YQ7d9vSLD4KCGasTbBwUTOVTHd2\nhbvAJ7F3YHraYwhNTy6HxzZNwp1teGwCnnR7H4Xb1WPBmF2bI8DAvFp1sA63A4I8ipjObeOjeBq4\nW0RuwJieAFDVL9dNqphJxozCxq4fHutyOyzDY3Fb4UE44qt9XBJmFCkRq6KTrmNfIiYBA5GU25nZ\nTwV/KaC7vuK4QWgGcX5GUeNBTkLCnW14rFn72+GGEM7y7KKeHG+KVeZ/yRzo8FC8mSrhRvGBTTQ2\nmdmfARCRSeaj7qy7VDGTC4ZSrs8obDpXcN+uD3bVY91thUFsM7MdX7gIzOg1CWVTahEl89/tKwLE\nmJldc8gsIotE5GHgIeBhEXlQRI6uv2jxUZ5RuHvr2JS0ToINOW27wh1utwMi5lE435bm8FGU16Oo\ncWACdKLEqClsTE8/BP5FVe8EEJFXAT8CXlxPweIklwQfRSpKHoXD7YgQHuv6KNzWR0ECZkfN4qOw\nXa0vCZgV7hyMegoohEoCQFXvAvL1Eyl+SjMKh8uMpy1GfEnwUdiHxyYjSc12RuGyORDsZkfl+lvu\nEiU81vFLYj8QqQOjzihE5Kjg7V9E5LvANZh7/P8Ad9RftPjIhZnZDpueopXwcLgdljbkpEQKWWdm\n11+ccZFOSU3lnQTC37kpSnjgZgmPLw37/OmK90n4XcdM+IBkHZ5RRCvh4W63FKV6rOumJ7CsHktz\nKL3SbofbEmlG4XJDCE1P8TCqolDVExopiEuEUU8u13qyMXMkwfQUpXpsEjpXmyc5CRFcqVQyVrCr\nhXVRwCS01eUV7kRkCmYluoWVx6vqh+onVryUZhSum55q5lGUj3UV6/BYEtC5WpZYcH3hIrArEVOK\nenL4ykRdW8Nlyjkhjb9/bKKebgbuAR7GlAVvesLMbJdnFCK1O9cklBm3Do9VtxUeRMzMrr844yJK\nUUCXKa/WZ5FH4fhFqVx5sNGy2iiKNlX9WN0lcYgkrEeRtqgeW4pKcfgJsPVRFBPQu5qlUC1mFAmY\nHtkUBQzb6vDtZb1wURIsT5XlSFINvoFsesKrReQ9IjJHRKaGf3WXLEbyCfFR1I56Co+tvzxjJexk\naq994HzfapUEWTrW8dbYFAVMArar9SWBkukphnPbKIoh4AvA34Elwd/iiTi5iPyriKiITA8+i4h8\nXURWiMhDFSG6DSWfCB9Fc0Q9hcrYZnbk8swIsF4VLjELF1mGPbncFIngo3C5HbC76anR2Jie/hXY\nX1U3TeSJRWQ+8HpgVcXmU4EDgr+XAd8OXhtKeYU7d01PNqPXJEQ9lZZCtage637naumjwP1OyTYn\nxHVsfRRJaKqUzGiNl9amJwzXr55ovgJcwu7X6Czgp2q4B5giInPqcO6qNE+Zcfd9FFHCY12eGUGE\nzOwkhPralIgJXl2+v5pt4SJwd0bRi1kv+3ZgMNw4nvBYETkLWKOqDw67yeYCz1V8Xh1sWzvCd1wE\nXASwYMGCsYoyIvkEFAW0MQ0kwfRkW4unmIBs5nCWV8tMloiFiyxLprtOFB+FywoPyn4tVxXFdcFf\nJETkNmD2CLsuBz6JMTuNGVW9CrgKYNGiRRP60+UTUOvJpgx0kkxPNqNXx5/j0gy0qFBtjJGEGYVE\nGIi43Jaoa7K7TKkSbgymJ5v1KH4iIu3AAlVdbvvFqnrSSNtF5EXAPkA4m5gH3C8ixwBrgPkVh88L\ntjWUfGLWo6h+TBIS7sI8itolPMB1y37omM8ViqRT6VGPS4LSS8e4mlo9aIb1KMpZ5o0/t816FGcA\nS4HfB5+PDJZFHROq+rCqzlTVhaq6EGNeOkpV1wE3AOcF0U8vB7ar6gvMTvUmCetRRCnh4fITYB8e\n636kUBglZzcSd7sxKbGr6AtutyRVHoZXJQlLoZZNTw7OKIArgGMIKsaq6lIR2bdO8twMnEbZgX5B\nnc5TlfJ6FA6bnqwWLtLSsa5iHx7rtgkNylFy+ZpDPveVnu2yrq7TVHkUdjqvLtgoipyqbh/m6Jmw\nUh7BrCJ8r8DFE/XdY6UQ2EFcnlGIzYgvAQl3tuGxxQRUjw1NlTbXxe2WmOuSr2EPbCYfBeD8RSmF\nx7poegKWichbgbSIHCAi/w38rc5yxUoSVrizyZxNgo8iVMY2nZLDzQDKs6N8Da1XTEDCnbm/4pZi\n/IQ/s21yqstUFgVsNDaK4oPAYZjQ2GuAHcBH6ilU3BSKSjolTofLZdOpkkIbjSQUBQzradVqSxKS\n1LIlpVe7LS4rb7CcsZaPrrc4Y6acpJZ8nM6jUNU+TEjr5fUXxw1yxaLTdZ4AWtIphvKFqscUE+Cj\nCDvXXI1RuJlRuNsOKPsomsH0ZFN0MgmUfNlNEPVkW+CwHlRbCrVqZJOqnjnx4rhBoaBkHVcU2bTU\nHoUnwfQUOoBrKYoEOIBDU2XNGUUC6lbZrUfhviIpVyd2X9Za2C7CVA+qzSiOxWRJXwP8A/cV7oSR\nD0xPLtOSSdUchSch4a48o7AYhTvcDrD3USShy7JZGCvE5esSZ+7BRFP2UTT+3NUUxWzgdcBbgLcC\nNwHXqOqyRggWJ/li0em1KCD0UdRSFObV5dGriJBJiYXpyf2oJ1sfBQlQerar9blOqpQtn/wSHpRM\nTw45s1W1oKq/V9XzgZdjchvuEJEPNEy6mMgX3J9RZNMpBvN2MwrX7/9MWqwcwK63w9pHgfvrUWTT\nKatrAm6bGrKWuS3JMKMFbxybUSAircDpmFnFQuDrwO/qL1a85Ivq/IzCxvREAnwUYB5mK2d2g+QZ\nK9F8FI2QaOwYH1jyVz62NQeC+wORcHARhxmtmjP7p8DhmGzpz6jqIw2TKmbyhWREPdmGxzreFLIW\nSi8JIaVRfBRutwQy6RS5GjNWEmDatFXeSaCcme2WM/vtmBLjHwY+VHEzCCaJelKdZYuNfFGdzsoG\nYxooFLWU8zESSUi4A/Mw522UntvNiDCjcH/0mk2nGKpxTZJAKiWWdavcpxzq2/hzj6ooVNVt20sd\nyRfU6axsgGymdqXSpPgobJIHcV9PkEnb+ijcD49tSVuU8EhAUUAwIdg17y/cb0fZ9OSQM3tPJl9U\npwsCgjE9AQxVMXMkoSgg2NnDk9C5pqPMKBoh0DiwMj0lhExaSvXbRiMBvuxYM7Pd7g1jIl8sOm96\naskEiqLKw5wY01M6ZVfrqUHyjJVMlDwKxxtjM8tLQlFAMArcZkbhOhnb8Os64BXFCBSKCTA9lWok\nVVMUyXBmZywe5CSsmW07ozBmNLfb0pIWcsViIsJGaxH682rh+ozVtopBPfCKYgRyhWJiTE+5/OgP\nQDEhIz7bLHPX25GN5KNohERjJ5tOoVq9LeV1sdxuTDpl729xGeuEzjrgdm8YE9UiiVwhm7H3Ubg/\nUqod9eT+Y1w2DTRFTohlVd8kYHN/JYHyjGIPURQicoWIrBGRpcHfaRX7LhORFSKyXEROjkO+XMH9\n8NjWQFEM5EavIJuEooBgOqVqCg+SUT3W5ppAMrLMSzW4qozES5nZjrfFOLOTH/WUtrgm9cJmhbt6\n8RVV/WLlBhE5FDgXs/7FXsBtInKgqlZ/8iaYJPgoOlvMpesbGv2nSYqPojWTYsdAvsZR6vyD3J41\nYcoDueTXrQqDJZoh8imTSpGzXA3SZbJ72oyiCmcBv1TVQVVdiakvdUyjhcgViqWpt6t0tJpOqXdo\n9A42KVFPHS1p+qu0AwJnttuXhLaSokj+jCI0c1QzPSXF0Z1J1Q6PTQKlqKc9zJn9ARF5SER+KCI9\nwba5mNLmIauDbS9ARC4SkcUisnjjxo0TKlgSZhRdrWZG0TtYTVEkI+GuoyVTdWYEyVgzu816RuG+\nmcN2QakkYBse6/pzUjYHNtGMQkRuE5FHRvg7C/g2sB9wJLAW+FLU71fVq1R1kaoumjFjxoTKbkp4\nuD187WgxnVLfYDUfRTIS7tpb0k0xCk+nhGxaGKix8iDgfGNKpqdqwRLBq+NNsQqPTcLcKM7w2Lr5\nKFT1JJvjROR7wI3BxzXA/Ird84JtDSVfLDo/owh9FE1hesqma84okjAKB2jLVFd6pUi0Rgk0RrIW\nmf9JIW2x3onB7auyxyXcicicio9vBMLKtDcA54pIq4jsAxwA3Nto+ZJQ66mz1d6Z7bieMD6KXKGq\nzdtkMzveEKCtJV3V9JSUbOb2YMbaX+X+SkoehfFRNIEzOx2fMzuuqKf/EpEjMc//M8B7AVR1mYhc\nCzwK5IGLGx3xBMmoHtuSSZFNC7uq+CiS0yllUDW2/bCDGo6qOh+9BdCWTVWfUQSvrneuNlF1SSHT\nJGtrlKsTN5HpqRqq+o4q+64ErmygOC8gn4DMbDBO4GrO7KT4KEr+lqF8FUXhumHAYG16crwx4TWp\nNhAJ1Z7rbWnPptm0a6jmca63IxtjEqT7vWEM5BOQmQ0wtbOFLb2jPwBJ8VG0lxRFtZG4+9VjwUQ+\n2c0o3KZs2qyV3+I+7YFpszru25721PBYZ8kXtBSK5jLTOlvYtGtw1P1JSbib1GY6pZ1Vku6SMqPo\nbsvUbAe4P3rtDJR3b9WoOvPqelvas5mqvpakUMpt2VOc2a5jaj25/9NM72qtOqUuFwV0+0nu6WgB\nqDo7SsKqcABTOrJs68+Nur+02I/jjeloqhlFymJG4f5AJOtnFG6RKxYTMaOY3t3C5iozCk3IjGJa\nVysAm3urtCUhpqfJ7S1s66uiKNy3cAAmZBlqzCiCV9cd8x0ttWcUSbgutgmd9cArimEUi4oqifBR\nTO9qZWtfbtTFi4oJcWZP6zQzis01Zkdut8IwpSPL9v6hmuUtHL8kpFJCR0u6hjM7GbRljY+iGIPJ\nZiJpzaRISTyzPK8ohhFWZsw6npkNsPe0DgBWbekbcX9S1qOY3J4lnZKqpicSYnrq6ciSKyi9o4xg\nk5J7ALWDJZLiowgjuAZrFDh0vR0iQmdLpuosr1643xs2mDAxJwkziv1mdAHw1MZdI+5PSpnxVEqY\n3tXC2u0Dox6juF/rCWBK6G8ZZXakCQkpBZjR3crGnaObA5NCWNW3mp8iCaYnMMVA/YzCAcIYZdcz\nswH2DRTFk+t3jrg/KZnZAAundfLM5t5R9yfFmT1vSjsAq7eNPMsrzyjcZ0ZXdUVRUnqNEmiMhAU0\nd1QJMoBkzPI6WzKjzlbriVcUwwhnFElQFF2tGfaf2cW9z2wdcX9SEu7AKL2nR5kZgXGcJqEd86cG\n5sDNoyiK4DUBTWFGdysbdo4+y0sK07oCH1iNYIkk0NGapi8Gv5FXFMMIQ89crx4b8qr9p3Pfyi0j\nRnUkJeEO4ICZXWzty7F2e/+I+5OwZjbAnMltZFLCM6MpilJRQPcbM7enna19ObaPMhJPio9iehBV\nZwu+cesAABBnSURBVJOd7TodLZlYAgyS0Rs2kHyCZhQApx4+m/5cgeuXvrDIblIS7gBetu9UAP62\nYvOI+5NiQ86kUxw0u5sHn9s24v4kzSgOnt0NwBOjmDaTQqgoqkXVQTKuyZT2LFv7Gq/wvKIYRliZ\nMSkzimP2mcrhcyfxldueYPuw+P2kJNwBHDJ7EjO7W7nxoedH3G/Wo3C/HQAvXTiVB57bOmIpj6Qo\nPICDZ08C4OHV22sc6fZ1mdrZQkpg3SizVUjOdZk9uY31OxofYJCM3rCBhJUZkzKjEBGuPPtFbOkd\n4oIf37tbAl5SKq6CiXx668sWcPvyjSx+ZssLD1D318wOed2hsxjIFbn54bUv3Jkg5b3XlHb2ndHJ\nnx/fMOL+hPSttGRS7D2tkyfWj+4DSwqzJrWxvT9Xc6GvicYrimGUTE8JyMwOOWL+FL5+7kt45Pkd\nnPK1O/nNktUUihrY9ZPTjncfty/zetp538+W8MCq3R30xpkdj1xROXbfaRw0q5sv3rr8BbW4khIp\nFPKGF+/F3U9tYtnzo88qknCLHTSrm0fX7qh6TAKawZzJbQA8N0ruVL3wimIY+QSFx1Zy6ovmcN2/\nvJI5k9v4+K8f5IQv3sG373iq5oItLtHVmuEn7zqGlnSKf/r23/jYtUt5ZM12VJOl9FIp4T/PeTFb\n+oZ447fu5rZH15eygpPiAA658JX7MLWjhff/7H6Wr9vdV1Er+9wlXrH/NFZt6Rs1lDwpLTl87mQA\nHqxpDpxYvKIYRtn0lLyf5tC9JnH9xa/kO28/ijmT2ygqiahZVcl+M7r4/UdfzTtfsQ+/f2Qdb/jv\nu3jtl/7CI2t2lJzzSeDI+VP4+btfTiaV4t0/XczxX7ydz974KHeu2AQkY/QKMLkjy/fOX0TvYJ5T\nv/ZX3nf1Eq5fuqZqjTEXOeXw2bRlU3zulscTvYjRfjO66OnI8qfH1jf0vHGtcIeIfBC4GCgAN6nq\nJcH2y4ALg+0fUtVbGylXaHpKJ6yDDRERTjl8DqccPocVG3bGUkBsvExqy/JvZxzKh088gJseXsvN\nD69l5aZe5kxuj1u0SBy9dw+3fuTV3LpsHb9espqr73mWH9y1EkhG5n/IUQt6uPWjr+Z7dz7Nb5es\n4ffL1gGmpDokQ+nN7G7j0lMO5or/fZRTvvpXzj5yLkfMn8JBs7uZEURFJWHGmk4J5x6zgG/f8RRX\n3/Ms/7xoHq2ZkRf7mkgkjumjiJwAXA6crqqDIjJTVTeIyKHANcAxwF7AbcCBtZZDXbRokS5evHhC\nZLt35Rb++bt/52cXvoxXHTB9Qr7TM34GcgVa0ilSCepgh9M3lOcfT2/hsXU7OOfoeczsbotbpMgU\ni8qDq7fxj5VbWLpqG9v6h/jRO48ZdWVC17h12Tq+fcdTLK0IX25JpxAxYbR3X/raGKWzYyBX4MKf\n3MfdKzbTnk3zgdfuz8Un7D+m7xKRJaq6qNZxcc0o3g98XlUHAVQ1DKs4C/hlsH2liKzAKI2/10OI\nvzyxkc/e+Ohu28LEtSSN+PYEwhLLSaajJcMJB8/khINnxi3KmEmlhJcs6OElC3riFmVMnHzYbE4+\nbDZbe4d4bN0OVmzYxZpt/azdNsDhcyfFLZ4Vbdk0V7/rZdy5YhO3P76hVPOtnsSlKA4EjhORK4EB\n4OOqeh8wF7in4rjVwbYXICIXARcBLFiwYExCdLVmOGhW9wu2v3L/aYm5aTweT3R6Olt4xX7TecV+\nybQapFLC8QfO4PgDZzTkfHVTFCJyGzB7hF2XB+edCrwceClwrYjsG+X7VfUq4CowpqexyHj03j0c\nvXcyR0Yej8fTKOqmKFT1pNH2icj7gf9R4yC5V0SKwHRgDTC/4tB5wTaPx+PxxERcMaDXAScAiMiB\nQAuwCbgBOFdEWkVkH+AA4N6YZPR4PB4P8fkofgj8UEQeAYaA84PZxTIRuRZ4FMgDF9eKePJ4PB5P\nfYlFUajqEPD2UfZdCVzZWIk8Ho/HMxrJSz/2eDweT0PxisLj8Xg8VfGKwuPxeDxV8YrC4/F4PFWJ\npdbTRCMiG4Fnx/jv0zGhuc2Ab4ubNEtbmqUd4NsSsreq1kzvbgpFMR5EZLFNUawk4NviJs3SlmZp\nB/i2RMWbnjwej8dTFa8oPB6Px1MVryiCwoJNgm+LmzRLW5qlHeDbEok93kfh8Xg8nur4GYXH4/F4\nquIVhcfj8XiqskcrChE5RUSWi8gKEbk0bnlGQkR+KCIbgkq74bapIvJHEXkyeO0JtouIfD1oz0Mi\nclTF/5wfHP+kiJwfQzvmi8jtIvKoiCwTkQ8nuC1tInKviDwYtOUzwfZ9ROQfgcy/EpGWYHtr8HlF\nsH9hxXddFmxfLiInN7otgQxpEXlARG5MeDueEZGHRWSpiCwOtiXu/gpkmCIivxGRx0XkMRE5Nta2\nqOoe+QekgaeAfTHrYTwIHBq3XCPI+WrgKOCRim3/BVwavL8U+M/g/WnALYBgVg/8R7B9KvB08NoT\nvO9pcDvmAEcF77uBJ4BDE9oWAbqC91ngH4GM1wLnBtu/A7w/eP8vwHeC9+cCvwreHxrcd63APsH9\nmI7hHvsY8AvgxuBzUtvxDDB92LbE3V+BHD8B3h28bwGmxNmWhjbepT/gWODWis+XAZfFLdcosi5k\nd0WxHJgTvJ8DLA/efxd4y/DjgLcA363YvttxMbXpeuB1SW8L0AHcD7wMkx2bGX5/AbcCxwbvM8Fx\nMvyeqzyugfLPA/4EvBa4MZArce0IzvsML1QUibu/gMnASoJgIxfasiebnuYCz1V8Xh1sSwKzVHVt\n8H4dMCt4P1qbnGprYLJ4CWYknsi2BOaapcAG4I+YUfQ2Vc2PIFdJ5mD/dmAabrTlq8AlQDH4PI1k\ntgNAgT+IyBIRuSjYlsT7ax9gI/CjwCT4fRHpJMa27MmKoilQM1RITIyziHQBvwU+oqo7KvclqS2q\nWlDVIzEj8mOAg2MWKTIi8gZgg6ouiVuWCeJVqnoUcCpwsYi8unJngu6vDMbc/G1VfQnQizE1lWh0\nW/ZkRbEGmF/xeV6wLQmsF5E5AMHrhmD7aG1yoq0iksUoiZ+r6v8EmxPZlhBV3QbcjjHRTBGRcNXI\nSrlKMgf7JwObib8trwTOFJFngF9izE9fI3ntAEBV1wSvG4DfYRR4Eu+v1cBqVf1H8Pk3GMURW1v2\nZEVxH3BAEOHRgnHO3RCzTLbcAIQRDOdj7P3h9vOCKIiXA9uDqeqtwOtFpCeIlHh9sK1hiIgAPwAe\nU9UvV+xKYltmiMiU4H07xtfyGEZhnBMcNrwtYRvPAf4cjAhvAM4Noon2AQ4A7m1MK0BVL1PVeaq6\nEHP//1lV30bC2gEgIp0i0h2+x9wXj5DA+0tV1wHPichBwaYTgUeJsS17dGa2iJyGsdGmgR+qWa/b\nKUTkGuA1mFLC64FPA9dhIlMWYMqr/7Oqbgk6428ApwB9wAWqGoYJvgv4ZPC1V6rqjxrcjlcBdwIP\nU7aHfxLjp4i9Lffff//ZmUzmUlWdXevYXC7Xsm3btmkYRy5tbW293d3d2/P5fGbbtm0zisViKpPJ\nDPX09GwSEVVV2bp16/R8Pt+SSqWKU6ZM2ZjJZPIAO3funNzf398FMGnSpC1tbW39NvKKyLp8Pv/5\no4466rpxNLvy+14DfFxV3yAi+2JmGFOBB4C3q+qgiLQBV2P8S1swkVFPB/9/OfAuII8xK94yEXJF\nkH9fzCwCjOnmF6p6pYhMw4H7KyoiciTwfUzE09PABZiBfSxt2aMVhccT8uCDDz61//7796dSKXp7\ne6eoqrOzbVUll8tlVq1alfnc5z53DnD7DTfc4B9kT93I1D7E49kjSLe2tuY3bdo0V0SKIuJ0x5vN\nZosikgbeibFVP1L9PzyeseMVhccTUCgU0gCpVKoQtyw2BMqsgDFLejx1w9nptcfTjJx22mnTtm7d\nKuHnSy65ZNLMmTPnnH766dNOPvnk6Z/61Ke6AdasWZM6/vjjp8+bN29OLper9pVSbafHMxF4ReHx\nNJATTzxx4JZbbmkLPy9evLjl6KOPHrruuus233rrrZuWLVuW3bJli0ydOrX4u9/9bvMRRxwxFKe8\nHg9405PHsxtf+POqzic39afH8x0HzOjIXXrSwh0j7TvzzDMHPvvZz05661vf2n///fdnDznkkNwz\nzzyTASgUCuTzeWlpaaG9vZ329nan/SSePQc/o/DssYjINDGVRpeuX79+3vLlyw/O54batJDPaiGf\nrcc5Fy5cKGvXrm3p7+/nxhtvbDv99NMHAM4+++xpr3rVq2bstddeha6urpoKoq+vryOXy7XUQ0aP\nZzh+RuHZY1HVzcCRAHfddde2np6e/stOnplJp9NVnQLjobe3t/u4444b+POf/9x65513tl5yySU7\nv/nNb3Zdd911m7PZLJdeeumkO++8s+W4446ranJqb2/vz+VyXfWS0+OpxCsKj2cE1q9fP3vWrFnr\nhoaGWnbt2tUtIsV8Pp9ta2vrz2Qy+b6+vk5VlZ6eni3pdLpQLBZTO3bsmBxGTnV3d29vaWnZTeGo\nquTz+cxZZ5219ZOf/OTkOXPmyODg4BRVzW7atGlmT0/Pjq6uruzzzz/ftnXr1nxPT88WgF27dnUX\nCoU2gNbW1sHu7u4dIqIiUuzr65ve0dHR+B/Is0fhTU8eTw3y+Xx28uTJ22fMmLFhYGCgo1AoZKZN\nm7apvb29r7e3txNgx44dkzo6OnqnTZu2acqUKVt37NgxZfj35HK5bCaTyR9xxBH5devWpU8++eR8\noVBIi0juwgsvLL7pTW/qeeyxxzj77LM35HI5PfPMM2c8/vjj2fPOO6/zqaee2jZ9+vSNXV1dO8Pv\nS6VShcHBwZmN/C08eyZ+RuHx1CCTyQylUqkiQDqdzre0tAwAZLPZ3NDQUAvA0NBQa6FQKPk1VDWl\nqlKZuFcoFNLh9yxZsmTDrl27ugG96aabdgGsX79+zqxZszYDdHR05K+55pqhzs7O3s2bN8/IZDKd\nAwMDA21tbQPh96VSKS0UCn464ak7XlF4PDUwpXRG/Vz6MHXq1I3Djx32f6qqMnxbxccRndjTpk3b\nODg42DowMNDe19fXOXXq1M1gSnmISCKSAz3JxpuePB5Mff/x1D1raWkZ7Ovr6ww/53K5FwzCMplM\nPvRhRJBLisViqrW1dXDSpEnb8/l8JthOoVBIZbPZbWMW2uOxxM8oPB5g/fr1G2bMmDEzm83max/9\nQiZNmrRjx44dkzdt2tQB0NLSMpTNZrdXHpPJZPLFYvEFJqlqBJVnp4Yzke7u7h2qys6dO9tWrVpF\nR0dHUtZQ8SQYXz3W4wGWLFkyM5/PX1MoFF6aTqeLtf9jbAz9/+3dMQrCMBjF8VckWJd0cXPyDL1H\nPJMH6Bm8Q47g6O4m6GwnaVyKNA5dFCSIVgry/x0gvO3xQZKvbadZlkVjzMcvrmOMsa7rS1VVV2vt\nTtLGe78dMCbwhIkCkFSW5dk5t5K0Vv/J3k++zui6btI0zbIoisM354QQFnmeB/X7H47DpANeY6IA\nHjjn5urXTs7GzvKGm6S99/40dhD8N4oCAJDErScAQBJFAQBIoigAAEkUBQAg6Q6fnqEamiqsJQAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46ede44c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7F1d24H4xgtQ1+d2I2BV4mdRt0Yq6G1pb9bZLu5P2AmCLwuNJeVndMpJGkL5G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\nESdtM7MS+V8Yt3dWT70jvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46eaa59c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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O6JUyFKpqxT2BvI7V4KHPi03jRmD22QDY4xuqnT2cofBD678nvqAlyr4hn8pi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\nOzvbh4aGWm655Zbo2WefPR4IBDKpVGpWPB+Hww9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46eaa9cf90>"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "cells, cells_to_stimulate, params, muscles = run_c302('NMJ','C2','',1000,0.05,'jNeuroML_NEURON',verbose=False,plot_ca=False, data_reader=\"UpdatedSpreadsheetDataReader\", config_package=\"notebooks.configs\")"
   ]
  },
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   "metadata": {
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   "source": [
    "### Interpreting the results\n",
    "\n",
    "The plots follow the same pattern as above, the first two from the neuron and the second two from the muscle.\n",
    "\n",
    "Observing the results, there is stepped input that is coming into the muscle cell from the neuron, that can only be getting there by passing through a synapse.  In fact the log output told us this above where it says `VB1-MVL07 2 exc Acetylcholine`\n",
    "\n",
    "\n",
    "While the output of the neuron ramps up to higher and higher membrane potential, the synapse filters that out and the muscle only responds to the change in input it is receiving.\n",
    "\n",
    "Since the \"C2\" configuration was used, the biological parameters are the same as we saw earlier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Conclusions so far and next steps\n",
    "\n",
    "At this point we are able to see some basic simulations working.  Even though we know the parameters that are used are still rather unphysiologically realistic, we are able to see that the tools that we are using are giving us results that match our expectations.\n",
    "\n",
    "A next step with this set up would be to flesh this notebook out with examples of the real recordings that come out from papers that have made recordings from the real cells in almost exactly this kind of setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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